Last updated: 2024-02-27

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Knit directory: PD1_mm/

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Untracked files:
    Untracked:  Layer1_DoHeatmap.pdf
    Untracked:  Layer1_UMAP.pdf
    Untracked:  Layer1_UMAP_DotPlot.pdf
    Untracked:  Layer1_scImmuCC_label.csv
    Untracked:  Layer1_tSNE.pdf
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    Untracked:  Layer2_Bcell_UMAP_DotPlot.pdf
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    Untracked:  Layer2_DC_UMAP_DotPlot.pdf
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Unstaged changes:
    Modified:   analysis/annotate.Rmd
    Modified:   analysis/comparative_analysis.Rmd
    Modified:   analysis/processing.Rmd
    Modified:   cluster_markers.tsv
    Modified:   code/utilities.R

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File Version Author Date Message
Rmd 18c1bd4 heinin 2024-02-27 Cell type proportion testing
html 18c1bd4 heinin 2024-02-27 Cell type proportion testing
Rmd 3e207b9 heinin 2024-02-26 Added scImmuCC annotations
html 3e207b9 heinin 2024-02-26 Added scImmuCC annotations
Rmd 407ac35 heinin 2024-02-26 Updating the first script
html 407ac35 heinin 2024-02-26 Updating the first script
Rmd 196db6a heinin 2024-02-26 Starting the comparative analysis
html 196db6a heinin 2024-02-26 Starting the comparative analysis
Rmd 627f4bf heinin 2024-02-23 Updated scripts
html 627f4bf heinin 2024-02-23 Updated scripts
Rmd 9e080d7 heinin 2024-02-23 Added the initial annotation script
html 9e080d7 heinin 2024-02-23 Added the initial annotation script

Introduction

Initial analysis on the scRNAseq data from Kluc tumors treated with PD1 and/or CAR T.

Packages and environment variables

suppressPackageStartupMessages({
  #library(cli)
  library(Seurat)
  library(SeuratObject)
  library(SeuratDisk)
  library(tidyverse)
  library(tibble)
  library(ggplot2)
  library(ggpubr)
  library(ggrepel)
  library(workflowr)
  library(googlesheets4)
  library(scImmuCC)})

setwd("/home/hnatri/PD1_mm/")
set.seed(9999)
options(ggrepel.max.overlaps = Inf)

# Colors, themes, cell type markers, and plot functions
source("/home/hnatri/PD1_mm/code/utilities.R")
source("/home/hnatri/PD1_mm/code/PD1_mm_themes.R")
source("/home/hnatri/PD1_mm/code/CART_plot_functions.R")

Importing data

seurat_data <- readRDS("/tgen_labs/banovich/BCTCSF/PD1_mm_Seurat/PD1_mm_Seurat_merged.Rds")

Adding human gene names to a separate assay

# Converting mouse gene names to human
mouse_human_genes <- read.csv("http://www.informatics.jax.org/downloads/reports/HOM_MouseHumanSequence.rpt", sep="\t")

convert_mouse_to_human <- function(gene_list){
  gene_names <- as.data.frame(matrix(nrow = length(gene_list),
                                     ncol = 2))
  colnames(gene_names) <- c("mouse", "human")
  rownames(gene_names) <- gene_list
  gene_names$mouse <- gene_list
  
  for(gene in gene_list){
    class_key = (mouse_human_genes %>% filter(Symbol == gene & Common.Organism.Name=="mouse, laboratory"))[['DB.Class.Key']]
    if(!identical(class_key, integer(0)) ){
      human_genes = (mouse_human_genes %>% filter(DB.Class.Key == class_key & Common.Organism.Name=="human"))[,"Symbol"]
      
      if(length(human_genes)==0){
        gene_names[gene, "human"] <- NA
      } else if (length(human_genes)>1){
        #  human_genes <- paste0(human_genes, collapse = ", ")
        bind_df <- data.frame("mouse" = rep(gene, times = length(human_genes)),
                              "human" = human_genes)
        gene_names <- rbind(gene_names, bind_df)
      } else {
        gene_names[gene, "human"] <- human_genes
      }
    }
  }
  return(gene_names)
}

gene_names <- convert_mouse_to_human(rownames(seurat_data@assays$RNA))
Warning: There was 1 warning in `filter()`.
ℹ In argument: `DB.Class.Key == class_key & Common.Organism.Name == "human"`.
Caused by warning in `DB.Class.Key == class_key`:
! longer object length is not a multiple of shorter object length
length(rownames(seurat_data@assays$RNA))
[1] 32285
dim(gene_names)
[1] 36196     2
# Keeping mouse genes with a single human ortholog
gene_names <- gene_names %>%
  group_by(mouse) %>%
  filter(!is.na(human),
         n() == 1) %>%
  ungroup()

assay_data <- LayerData(seurat_data, assay = "RNA", layer = "counts")
assay_data <- assay_data[which(rownames(assay_data) %in% gene_names$mouse),]
new_names <- rownames(assay_data)
new_names <- mapvalues(x = new_names,
                       from = gene_names$mouse,
                       to = gene_names$human)
rownames(assay_data) <- new_names

seurat_data[["RNA_human"]] <- CreateAssayObject(assay_data,
                                                min.cells = 0,
                                                min.features = 0)
Warning: Non-unique features (rownames) present in the input matrix, making
unique
saveRDS(seurat_data, "/tgen_labs/banovich/BCTCSF/PD1_mm_Seurat/PD1_mm_Seurat_merged.Rds")

Visualizing clusters

DimPlot(seurat_data,
        group.by = "snn_res.0.5",
        reduction = "umap",
        raster = T,
        cols = cluster_col,
        label = T) &
  coord_fixed(ratio = 1) &
  theme_bw() &
  NoLegend() &
  manuscript_theme

Version Author Date
18c1bd4 heinin 2024-02-27
3e207b9 heinin 2024-02-26

Cell type marker expression

DefaultAssay(seurat_data) <- "RNA"

plot_features <- c()

# Top markers for each cluster
markers <- presto::wilcoxauc(seurat_data,
                             group_by = "snn_res.0.5",
                             assay = "data",
                             seurat_assay = "RNA")

top_markers <- markers %>%  group_by(group) %>% slice_max(order_by = auc, n = 2)

FeaturePlot(seurat_data,
            features = top_markers$feature,
            ncol = 5,
            reduction = "umap",
            raster = T,
            cols = c("gray89", "tomato3")) &
  coord_fixed(ratio = 1) &
  theme_bw() &
  NoLegend() &
  manuscript_theme

Version Author Date
18c1bd4 heinin 2024-02-27
3e207b9 heinin 2024-02-26
top_markers <- markers %>%  group_by(group) %>% slice_max(order_by = auc, n = 5)

# seurat_object, plot_features, group_var, group_colors, column_title, km=5, row.order = NULL
dotplot_heatmap <- create_dotplot_heatmap(seurat_object = seurat_data,
                                          plot_features = unique(top_markers$feature),
                                          group_var = "snn_res.0.5",
                                          group_colors = cluster_col,
                                          column_title = "",
                                          km = 5, row.order = NULL)

Version Author Date
3e207b9 heinin 2024-02-26

Saving top markers to a file

top_markers <- markers %>%  group_by(group) %>% slice_max(order_by = auc, n = 20)

write.table(top_markers, "/home/hnatri/PD1_mm/cluster_markers.tsv",
            quote = F, row.names = F, sep = "\t")

Canonical mouse immune markers

# Mouse immune markers
gs4_deauth()
canonical_markers  <- gs4_get("https://docs.google.com/spreadsheets/d/1ApwXjEVtpPB87al6q3ab8TKvZYJTh3iNH1cuO-A_OoU/edit?usp=sharing")
sheet_names(canonical_markers)
 [1] "Sample summary"                                   
 [2] "GSEA GBM"                                         
 [3] "Mm immune markers"                                
 [4] "Cluster annotations, JAK mouse"                   
 [5] "Sherri-Cluster annotations, JAK mouse"            
 [6] "Cluster markers, JAK mouse"                       
 [7] "Sherri-Cluster markers, JAK mouse"                
 [8] "DL_cluster_res"                                   
 [9] "Cluster markers, final data, UPN109pre, no UPN208"
[10] "Cluster annotations, GBM+mouse"                   
[11] "Cluster markers, GBM+JAK1KO"                      
[12] "Sherri-Cluster markers, GBM+JAK1KO"               
[13] "Cluster markers, final data, immune+fibroblast"   
[14] "Cluster annotations, immune+fibroblast"           
[15] "Cluster markers, immune+fibroblast, top 100"      
[16] "Sherri - Cluster annotations, immune+fibroblast"  
[17] "Sherri-Only GBM Charactarization 50 genes"        
[18] "Sherri only GBM immune+fibroblast, top 100"       
[19] "Cluster annotations"                              
[20] "Heatmap genes"                                    
[21] "Tumor and CSF sample summary"                     
mm_immune_markers <- read_sheet(canonical_markers, sheet = "Mm immune markers")
✔ Reading from "13384 tumor scRNAseq tables".
✔ Range ''Mm immune markers''.
dotplot_heatmap <- create_dotplot_heatmap(seurat_object = seurat_data,
                                          plot_features = mm_immune_markers$gene_name,
                                          group_var = "snn_res.0.5",
                                          group_colors = cluster_col,
                                          column_title = "",
                                          km = 5, row.order = NULL)
Warning: The following requested variables were not found: Cd51, Nirp3, 117r

Version Author Date
3e207b9 heinin 2024-02-26

Using sc-ImmuCC to annotate immune cells

count_data <- LayerData(seurat_data, assay = "RNA_human", layer = "counts")

#scImmuCC_Layered(count = count_data, Non_Immune = FALSE)

# Importing results
scicc_labels <- read.csv("/home/hnatri/PD1_mm/Layer1_scImmuCC_label.csv",
                         row.names = "X")

length(colnames(seurat_data))
length(intersect(scicc_labels$barcodes, colnames(seurat_data)))

seurat_data$scImmuCC_celltype <- mapvalues(x = colnames(seurat_data),
                                           from = scicc_labels$barcodes,
                                           to = scicc_labels$cell_type)

# Plotting
DimPlot(seurat_data,
        group.by = "scImmuCC_celltype",
        reduction = "umap",
        raster = T,
        #cols = scImmuCC_celltype_col,
        label = T) &
  coord_fixed(ratio = 1) &
  theme_bw() &
  NoLegend() &
  manuscript_theme

Version Author Date
18c1bd4 heinin 2024-02-27
3e207b9 heinin 2024-02-26
saveRDS(seurat_data, "/tgen_labs/banovich/BCTCSF/PD1_mm_Seurat/PD1_mm_Seurat_merged.Rds")
DimPlot(seurat_data,
        split.by = "scImmuCC_celltype",
        group.by = "snn_res.0.5",
        ncol = 3,
        reduction = "umap",
        raster = T,
        cols = cluster_col) &
  coord_fixed(ratio = 1) &
  theme_bw() &
  NoLegend()

Version Author Date
18c1bd4 heinin 2024-02-27
3e207b9 heinin 2024-02-26
table(seurat_data$scImmuCC_celltype,
      seurat_data$snn_res.0.5)
            
                0    1    2    3    4    5    6    7    8    9   10   11   12
  Bcell         0    2    1    0    0    0    0    2    1    1    3    5    0
  DC           33   31   17    3   75    3    0   10    2   71    4  796   37
  ILC           0    0    0    2    0    0    0    0    0    0    0    3    0
  Macrophage 2586 3867  990  115 1116   64   39 2437 1031 1492 1303  694  933
  Mast          1    2    0   23    1    4    1    1    0   83   78   10    3
  Monocyte   2230  703 2988   13 2151    6   10  159  820  644  495  374  480
  Neutrophil    1    0    0   19    0    4    5    0    0   74  435   22   13
  NK            0    0    0   46    0  731 2647    2  157    2   22    3    7
  Tcell         5   10    0 3245    2 2413   36    9  519    4   28   17   12
            
               13   14   15   16   17   18   19
  Bcell      1314    4    0  272    0    0    0
  DC            1  250   22   37    1    1    3
  ILC           0    3    0   35    0    0    0
  Macrophage   49  647  554  351   30  132  196
  Mast          0    0    0    1    0   17    0
  Monocyte     29  149  320   76   23   14  282
  Neutrophil    1   79    0    4    0   26    0
  NK            8   15   52    9   80  189    0
  Tcell        30   75  230   53  665  196    5

Adding annotations from the Google Sheet

gs4_deauth()
markers_annotations  <- gs4_get("https://docs.google.com/spreadsheets/d/1iWYBouwQlQboI-rwiujC0QKJ6lq9XeTffbKm2Nz8es0/edit?usp=sharin#g")
sheet_names(markers_annotations)
[1] "Cluster top markers" "Cluster annotations" "scImmuCC"           
[4] "Mm immune markers"  
annotations <- read_sheet(markers_annotations, sheet = "Cluster annotations")
✔ Reading from "PD1 mm scRNAseq tables".
✔ Range ''Cluster annotations''.
seurat_data$celltype <- mapvalues(seurat_data$snn_res.0.5,
                                  from = annotations$snn_res.0.5,
                                  to = annotations$annotation)

DimPlot(seurat_data,
        group.by = "celltype",
        reduction = "umap",
        raster = T,
        label = T) &
  coord_fixed(ratio = 1) &
  theme_bw() &
  NoLegend()

Version Author Date
18c1bd4 heinin 2024-02-27
FeaturePlot(seurat_data,
            features = c("percent.mt_RNA", "nCount_RNA", "nFeature_RNA"),
            reduction = "umap",
            raster = T,
            ncol = 3) &
  coord_fixed(ratio = 1) &
  theme_bw() &
  NoLegend()

saveRDS(seurat_data, "/tgen_labs/banovich/BCTCSF/PD1_mm_Seurat/PD1_mm_Seurat_merged.Rds")

sessionInfo()
R version 4.3.0 (2023-04-21)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 22.04.3 LTS

Matrix products: default
BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 
LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
 [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

time zone: Etc/UTC
tzcode source: system (glibc)

attached base packages:
[1] grid      stats     graphics  grDevices utils     datasets  methods  
[8] base     

other attached packages:
 [1] ComplexHeatmap_2.18.0 viridis_0.6.3         viridisLite_0.4.2    
 [4] circlize_0.4.15       plyr_1.8.8            RColorBrewer_1.1-3   
 [7] scImmuCC_1.0.0        GSVA_1.50.0           googlesheets4_1.1.0  
[10] workflowr_1.7.1       ggrepel_0.9.3         ggpubr_0.6.0         
[13] lubridate_1.9.2       forcats_1.0.0         stringr_1.5.0        
[16] dplyr_1.1.2           purrr_1.0.1           readr_2.1.4          
[19] tidyr_1.3.0           tibble_3.2.1          ggplot2_3.4.2        
[22] tidyverse_2.0.0       SeuratDisk_0.0.0.9021 Seurat_5.0.1         
[25] SeuratObject_5.0.1    sp_1.6-1             

loaded via a namespace (and not attached):
  [1] fs_1.6.2                    matrixStats_1.0.0          
  [3] spatstat.sparse_3.0-1       bitops_1.0-7               
  [5] doParallel_1.0.17           httr_1.4.6                 
  [7] tools_4.3.0                 sctransform_0.4.1          
  [9] backports_1.4.1             utf8_1.2.3                 
 [11] R6_2.5.1                    HDF5Array_1.30.0           
 [13] lazyeval_0.2.2              uwot_0.1.14                
 [15] GetoptLong_1.0.5            rhdf5filters_1.14.1        
 [17] withr_2.5.0                 gridExtra_2.3              
 [19] progressr_0.13.0            cli_3.6.1                  
 [21] Biobase_2.62.0              Cairo_1.6-0                
 [23] spatstat.explore_3.2-1      fastDummies_1.7.3          
 [25] labeling_0.4.2              sass_0.4.6                 
 [27] spatstat.data_3.0-1         ggridges_0.5.4             
 [29] pbapply_1.7-0               parallelly_1.36.0          
 [31] rstudioapi_0.14             RSQLite_2.3.1              
 [33] shape_1.4.6                 generics_0.1.3             
 [35] ica_1.0-3                   spatstat.random_3.1-5      
 [37] car_3.1-2                   Matrix_1.6-5               
 [39] fansi_1.0.4                 S4Vectors_0.40.2           
 [41] abind_1.4-5                 lifecycle_1.0.3            
 [43] whisker_0.4.1               yaml_2.3.7                 
 [45] carData_3.0-5               SummarizedExperiment_1.32.0
 [47] rhdf5_2.46.1                SparseArray_1.2.3          
 [49] Rtsne_0.16                  blob_1.2.4                 
 [51] promises_1.2.0.1            crayon_1.5.2               
 [53] miniUI_0.1.1.1              lattice_0.21-8             
 [55] beachmat_2.18.0             cowplot_1.1.1              
 [57] annotate_1.80.0             KEGGREST_1.42.0            
 [59] magick_2.7.4                pillar_1.9.0               
 [61] knitr_1.43                  GenomicRanges_1.54.1       
 [63] rjson_0.2.21                future.apply_1.11.0        
 [65] codetools_0.2-19            leiden_0.4.3               
 [67] glue_1.6.2                  getPass_0.2-4              
 [69] data.table_1.14.8           vctrs_0.6.2                
 [71] png_0.1-8                   spam_2.9-1                 
 [73] cellranger_1.1.0            gtable_0.3.3               
 [75] cachem_1.0.8                xfun_0.39                  
 [77] S4Arrays_1.2.0              mime_0.12                  
 [79] survival_3.5-5              gargle_1.4.0               
 [81] SingleCellExperiment_1.24.0 iterators_1.0.14           
 [83] ellipsis_0.3.2              fitdistrplus_1.1-11        
 [85] ROCR_1.0-11                 nlme_3.1-162               
 [87] bit64_4.0.5                 RcppAnnoy_0.0.20           
 [89] GenomeInfoDb_1.38.5         rprojroot_2.0.3            
 [91] bslib_0.4.2                 irlba_2.3.5.1              
 [93] KernSmooth_2.23-21          colorspace_2.1-0           
 [95] BiocGenerics_0.48.1         DBI_1.1.3                  
 [97] tidyselect_1.2.0            processx_3.8.1             
 [99] curl_5.0.0                  bit_4.0.5                  
[101] compiler_4.3.0              git2r_0.32.0               
[103] graph_1.80.0                hdf5r_1.3.8                
[105] DelayedArray_0.28.0         plotly_4.10.2              
[107] scales_1.2.1                lmtest_0.9-40              
[109] callr_3.7.3                 digest_0.6.31              
[111] goftest_1.2-3               presto_1.0.0               
[113] spatstat.utils_3.0-3        rmarkdown_2.22             
[115] XVector_0.42.0              htmltools_0.5.5            
[117] pkgconfig_2.0.3             sparseMatrixStats_1.14.0   
[119] MatrixGenerics_1.14.0       highr_0.10                 
[121] fastmap_1.1.1               GlobalOptions_0.1.2        
[123] rlang_1.1.1                 htmlwidgets_1.6.2          
[125] shiny_1.7.4                 DelayedMatrixStats_1.24.0  
[127] farver_2.1.1                jquerylib_0.1.4            
[129] zoo_1.8-12                  jsonlite_1.8.5             
[131] BiocParallel_1.36.0         BiocSingular_1.18.0        
[133] RCurl_1.98-1.12             magrittr_2.0.3             
[135] GenomeInfoDbData_1.2.11     dotCall64_1.0-2            
[137] patchwork_1.1.2             Rhdf5lib_1.24.1            
[139] munsell_0.5.0               Rcpp_1.0.10                
[141] reticulate_1.29             stringi_1.7.12             
[143] zlibbioc_1.48.0             MASS_7.3-60                
[145] parallel_4.3.0              listenv_0.9.0              
[147] deldir_1.0-9                Biostrings_2.70.1          
[149] splines_4.3.0               tensor_1.5                 
[151] hms_1.1.3                   ps_1.7.5                   
[153] igraph_1.4.3                spatstat.geom_3.2-1        
[155] ggsignif_0.6.4              RcppHNSW_0.5.0             
[157] reshape2_1.4.4              stats4_4.3.0               
[159] ScaledMatrix_1.10.0         XML_3.99-0.14              
[161] evaluate_0.21               foreach_1.5.2              
[163] tzdb_0.4.0                  httpuv_1.6.11              
[165] RANN_2.6.1                  polyclip_1.10-4            
[167] clue_0.3-64                 future_1.32.0              
[169] scattermore_1.2             rsvd_1.0.5                 
[171] broom_1.0.4                 xtable_1.8-4               
[173] RSpectra_0.16-1             rstatix_0.7.2              
[175] later_1.3.1                 googledrive_2.1.0          
[177] memoise_2.0.1               AnnotationDbi_1.64.1       
[179] IRanges_2.36.0              cluster_2.1.4              
[181] timechange_0.2.0            globals_0.16.2             
[183] GSEABase_1.64.0